Design and Implementation of a High-Availability Enterprise Data Integration System Using Automated ETL Pipelines
DOI:
https://doi.org/10.63282/3050-9416.ICAIDSCT26-124Keywords:
Enterprise Data Integration, High Availability, Automated ETL, Data Pipelines, Fault Tolerance, Scalability, Distributed SystemsAbstract
Nowadays, enterprise data integration is indispensable to organizations that need to operate in distributed, cloud-native, and hybrid environments in which data keeps flowing from various sources to analytics, operational systems, and AI-driven applications. As companies more heavily depend on data for making decisions, they turn the focus to the Extract, Transform, Load (ETL) pipelines and make their availability and trustworthiness the key success factors. Traditional batch-oriented ETL solutions that were nice and shiny and worked well with centralized systems do not always keep up with the demands of contemporary real-time processing, ask for scalability, and require resilience. Being inevitably limited as they are by rigid scheduling, single points of failure, a small number of recovery mechanisms, and still considerable downtime even after hardware or software failures, those old stick-in-the-mud approaches can hardly be relied on to deliver timely insights and lower operational risks. To cope with those issues, the research is invested in coming up with and installing an enterprise data integration infrastructure that is highly available and features automated ETL pipelines incorporating fault tolerance, elasticity, and continuous data flow as core characteristics. The scheme at hand relies on event-driven processing, pipeline component orchestration, failure recognition automation, and recovery support techniques, thereby allowing seamless data flow in the face of the partial breakdown of ND the system. Distributed execution, smart retry mechanisms, and on-demand workload balancing make for high availability, while automation reduces manual labor and thus the associated risks and costs. In addition to developing an architectural design, the method involves building fail-safe ETL elements and demonstrating these through a practical enterprise case study where multiple data sources are ingested and analytics is the go-to for data consumption. The case study results are utilized to pinpoint the real-world impact of automated, high-availability ETL architectures on enterprise data dependability, support for near–real-time integration scenarios, and a scalable base for advanced analytics and digital transformation projects.
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